{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T21:13:57Z","timestamp":1742937237481,"version":"3.40.3"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030164461"},{"type":"electronic","value":"9783030164478"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-16447-8_4","type":"book-chapter","created":{"date-parts":[[2019,3,29]],"date-time":"2019-03-29T23:04:19Z","timestamp":1553900659000},"page":"37-46","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Predicting Postoperative Complications for Gastric Cancer Patients Using Data Mining"],"prefix":"10.1007","author":[{"given":"Hugo","family":"Peixoto","sequence":"first","affiliation":[]},{"given":"Alexandra","family":"Francisco","sequence":"additional","affiliation":[]},{"given":"Ana","family":"Duarte","sequence":"additional","affiliation":[]},{"given":"M\u00e1rcia","family":"Esteves","sequence":"additional","affiliation":[]},{"given":"Sara","family":"Oliveira","sequence":"additional","affiliation":[]},{"given":"V\u00edtor","family":"Lopes","sequence":"additional","affiliation":[]},{"given":"Ant\u00f3nio","family":"Abelha","sequence":"additional","affiliation":[]},{"given":"Jos\u00e9","family":"Machado","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,3,31]]},"reference":[{"issue":"2","key":"4_CR1","first-page":"80","volume":"40","author":"A Biglarian","year":"2011","unstructured":"Biglarian, A., Hajizadeh, E., Kazemnejad, A., Zali, M.R.: Application of artificial neural network in predicting the survival rate of gastric cancer patients. Iran. J. Public Health 40(2), 80\u201386 (2011)","journal-title":"Iran. J. Public Health"},{"key":"4_CR2","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1007\/978-3-319-15826-6_2","volume-title":"Gastric Cancer","author":"M Rugge","year":"2015","unstructured":"Rugge, M., Fassan, M., Graham, D.Y.: Epidemiology of gastric cancer. In: Strong, V. (ed.) Gastric Cancer, pp. 23\u201334. Cham, Springer (2015). \n                    https:\/\/doi.org\/10.1007\/978-3-319-15826-6_2"},{"key":"4_CR3","doi-asserted-by":"publisher","first-page":"467","DOI":"10.1007\/978-1-60327-492-0_23","volume-title":"Methods of Molecular Biology","author":"H Brenner","year":"2009","unstructured":"Brenner, H., Rothenbacher, D., Arndt, V.: Epidemiology of stomach cancer. In: Verma, M. (ed.) Methods of Molecular Biology, pp. 467\u2013477. Springer, Heidelberg (2009). \n                    https:\/\/doi.org\/10.1007\/978-1-60327-492-0_23"},{"key":"4_CR4","doi-asserted-by":"publisher","first-page":"239","DOI":"10.2147\/CMAR.S149619","volume":"10","author":"R Sitarz","year":"2018","unstructured":"Sitarz, R., Skierucha, M., Mielko, J., Offerhaus, G.J.A., Maciejewski, R., Polkowski, W.: Gastri cancer: epidemiology, prevention, classification, and treatment. Cancer Manag. Res. 10, 239\u2013248 (2018)","journal-title":"Cancer Manag. Res."},{"issue":"Suppl 1","key":"4_CR5","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1007\/s10120-002-0203-6","volume":"5","author":"DM Roder","year":"2002","unstructured":"Roder, D.M.: The epidemiology of gastric cancer. Gastric Cancer 5(Suppl 1), 5\u201311 (2002)","journal-title":"Gastric Cancer"},{"issue":"5","key":"4_CR6","doi-asserted-by":"publisher","first-page":"700","DOI":"10.1158\/1055-9965.EPI-13-1057","volume":"23","author":"P Karimi","year":"2014","unstructured":"Karimi, P., Islami, F., Anandasabapathy, S., Freedman, N.D., Kamangar, F.: Gastric cancer: descriptive epidemiology, risk factors, screening, and prevention. Cancer Epidemiol. Biomark. Prev. 23(5), 700\u2013713 (2014)","journal-title":"Cancer Epidemiol. Biomark. Prev."},{"issue":"2","key":"4_CR7","first-page":"64","volume":"19","author":"HC Koh","year":"2011","unstructured":"Koh, H.C., Tan, G.: Data mining applications in healthcare. J. Healthc. Inf. Manag. 19(2), 64\u201372 (2011)","journal-title":"J. Healthc. Inf. Manag."},{"key":"4_CR8","volume-title":"Data Mining: Practical Machine Learning Tools and Techniques","author":"I Witten","year":"2005","unstructured":"Witten, I., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)","edition":"2"},{"key":"4_CR9","doi-asserted-by":"publisher","DOI":"10.1002\/9780470979174","volume-title":"Data Mining and Statistics for Decision-Making","author":"S Tuffery","year":"2011","unstructured":"Tuffery, S.: Data Mining and Statistics for Decision-Making, 1st edn. Wiley, Oxford (2011)","edition":"1"},{"key":"4_CR10","doi-asserted-by":"publisher","first-page":"565","DOI":"10.1016\/j.procs.2017.08.284","volume":"113","author":"F Fonseca","year":"2017","unstructured":"Fonseca, F., Peixoto, H., Miranda, F., Machado, J., Abelha, A.: Step towards prediction of perineal tear. Procedia Comput. Sci. 113, 565\u2013570 (2017)","journal-title":"Procedia Comput. Sci."},{"key":"4_CR11","unstructured":"B\u00e2ra, A., Lungu, I.: Improving decision support systems with data mining techniques. In: Advances in Data Mining Knowledge Discovery and Applications. INTECH Open Access Publisher, pp. 397\u2013418 (2012)"},{"issue":"2","key":"4_CR12","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1016\/S0167-9236(01)00139-7","volume":"33","author":"J Shim","year":"2002","unstructured":"Shim, J., Warkentin, M., Courtney, J., Power, D., Sharda, R., Carlsson, C.: Past, present, and future of decision support technology. Decis. Support Syst. 33(2), 111\u2013126 (2002)","journal-title":"Decis. Support Syst."},{"key":"4_CR13","first-page":"w14073","volume":"144","author":"P Beeler","year":"2014","unstructured":"Beeler, P., Bates, D., Hug, B.: Clinical decision support systems. Swiss Med. Wkly 144, w14073 (2014)","journal-title":"Swiss Med. Wkly"},{"key":"4_CR14","unstructured":"Trowbridge, R., Weingarten, S.: Clinical decision support systems [Internet], Chap. 53. United States Department of Health & Human Services Agency for Healthcare Research and Quality (2001). \n                    https:\/\/archive.ahrq.gov\/clinic\/ptsafety\/chap53.htm\n                    \n                  . Accessed 6 May 2018"},{"key":"4_CR15","doi-asserted-by":"publisher","first-page":"571","DOI":"10.1016\/j.procs.2017.08.287","volume":"113","author":"A Morais","year":"2017","unstructured":"Morais, A., Peixoto, H., Coimbra, C., Abelha, A., Machado, J.: Predicting the need of Neonatal Resuscitation using data mining. Procedia Comput. Sci. 113, 571\u2013576 (2017)","journal-title":"Procedia Comput. Sci."},{"issue":"6","key":"4_CR16","doi-asserted-by":"publisher","first-page":"1947","DOI":"10.1021\/ci034160g","volume":"43","author":"V Svetnik","year":"2003","unstructured":"Svetnik, V., Liaw, A., Tong, C., Culberson, J., Sheridan, R., Feuston, B.: Random forest: a classification and regression tool for compound classification and QSAR modeling. J. Chem. Inf. Comput. Sci. 43(6), 1947\u20131958 (2003)","journal-title":"J. Chem. Inf. Comput. Sci."},{"key":"4_CR17","unstructured":"Chen, C., Liaw, A., Breiman, L.: Using random forest to learn imbalanced data (2004)"},{"key":"4_CR18","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1016\/j.eswa.2017.04.003","volume":"82","author":"C Zhang","year":"2017","unstructured":"Zhang, C., Liu, C., Zhang, X., Almpanidis, G.: An up-to-date comparison of state-of-the-art classification algorithms. Expert Syst. Appl. 82, 128\u2013150 (2017)","journal-title":"Expert Syst. Appl."},{"key":"4_CR19","doi-asserted-by":"crossref","unstructured":"Khoshgoftaar, T., Golawala, M., Hulse, J.: An empirical study of learning from imbalanced data using random forest. In: 19th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2007) (2007)","DOI":"10.1109\/ICTAI.2007.46"},{"key":"4_CR20","volume-title":"Machine Learning","author":"T Mitchell","year":"1997","unstructured":"Mitchell, T.: Machine Learning. McGraw-Hill, New York (1997)"},{"key":"4_CR21","unstructured":"Platt, J.: Sequential minimal optimization: a fast algorithm for training support vector machines (1998)"},{"issue":"1","key":"4_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10115-007-0114-2","volume":"14","author":"X Wu","year":"2009","unstructured":"Wu, X., et al.: Top 10 algorithms in data mining. Knowl. Inf. Syst. 14(1), 1\u201337 (2009)","journal-title":"Knowl. Inf. Syst."},{"issue":"12","key":"4_CR23","doi-asserted-by":"publisher","first-page":"1955","DOI":"10.1016\/j.asr.2007.07.020","volume":"41","author":"Y Zhao","year":"2008","unstructured":"Zhao, Y., Zhang, Y.: Comparison of decision tree methods for finding active objects. Adv. Space Res. 41(12), 1955\u20131959 (2008)","journal-title":"Adv. Space Res."},{"issue":"2","key":"4_CR24","first-page":"201","volume":"5","author":"A Rajput","year":"2011","unstructured":"Rajput, A., Aharwal, R., Dubey, M., Saxena, S., Raghuvanshi, M.: J48 and JRIP rules for e-governance data. Int. J. Comput. Sci. Secur. (IJCSS) 5(2), 201\u2013207 (2011)","journal-title":"Int. J. Comput. Sci. Secur. (IJCSS)"},{"key":"4_CR25","doi-asserted-by":"crossref","unstructured":"Mohamed, W., Salleh, M., Omar, A.: A comparative study of reduced error pruning method in decision tree algorithms. In: 2012 IEEE International Conference on Control System, Computing and Engineering, pp. 392\u2013397 (2012)","DOI":"10.1109\/ICCSCE.2012.6487177"},{"issue":"2","key":"4_CR26","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1016\/j.artmed.2004.07.002","volume":"34","author":"D Delen","year":"2005","unstructured":"Delen, D., Walker, G., Kadam, A.: Predicting breast cancer survivability: a comparison of three data mining methods. Artif. Intell. Med. 34(2), 113\u2013127 (2005)","journal-title":"Artif. Intell. Med."},{"issue":"1","key":"4_CR27","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1186\/1472-6947-11-51","volume":"11","author":"M Khalilia","year":"2011","unstructured":"Khalilia, M., Chakraborty, S., Popescu, M.: Predicting disease risks from highly imbalanced data using random forest. BMC Med. Informat. Decis.-Making 11(1), 51 (2011)","journal-title":"BMC Med. Informat. Decis.-Making"}],"container-title":["Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","Intelligent Technologies for Interactive Entertainment"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-16447-8_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,5,21]],"date-time":"2019-05-21T01:41:03Z","timestamp":1558402863000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-16447-8_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030164461","9783030164478"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-16447-8_4","relation":{},"ISSN":["1867-8211","1867-822X"],"issn-type":[{"type":"print","value":"1867-8211"},{"type":"electronic","value":"1867-822X"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"31 March 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"INTETAIN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Technologies for Interactive Entertainment","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Guimar\u00e3es","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 November 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 November 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"intetain2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.allconferencealert.org\/intetain-2018\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Open","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"confy.eai.eu","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"23","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"15","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"65% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}}]}}